• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.2
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• pp.133-140
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• 2016

In active sonar field, a target detection and localization based on a distributed sensor network has been much studied for the underwater surveillance of the coast. Zhou et al. proposed a target localization method utilizing the positions of target-detected sensors in distributed sensor network which consists of detection-only sensors. In contrast with a conventional method, Zhou's method dose not require to estimate the propagation model parameters of detection signal. Also it needs the lower computational complexity, and to transmit less data between network nodes. However, it has large target localization error. So it has been modified for reducing localization error by Ryu. Modified Zhou's method has better estimation performance than Zhou's method, but still relatively large estimation error. In this paper, a target localization method based on modified Zhou's method is proposed for reducing the localization error. The proposed method utilizes the geometry of the positions of target-detected sensors and a line that represents the bearing of target, a line can be found by modified Zhou's method. This paper shows that the proposed method has better target position estimation performance than Zhou's and modified Zhou's method by computer simulations.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.211-221
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• 2012

The development of wireless sensor networks (WSNs) is progressed slowly due to limited resources, but it is in progress to the development of the latest IP-based IP-WSN by the development of hardware and power management technology. IPv6 over Low power WPAN (6LoWPAN) is capable of IPv6-built low-power devices. In these IP-based WSNs, existing IP-based techniques which was impossible in WSNs becomes possible. 6LoWPAN is based on the IEEE 802.15.4 sensor networks and is a IPv6-supported technology. Host-based mobility management scheme in IP-WSNs are not suitable due to the additional signaling, network-based mobility management scheme is more suitable. In this paper, we propose an enhanced PMIPv6-based route optimization scheme which consider multi-6LoWPAN network environments. All SLMA (Sensor Local Mobility Anchor) of the 6LoWPAN domain are connected with the SPIG (Sensor Proxy Internetworking Gateway) and performs distributed mobility control for the 6LoWPAN-based inter-domain operations. All information of SLMA in 6LoWPAN domain is maintained by SMAG (Sensor Mobile Access Gateway), and then is performed the route optimization quickly. The status information of the route optimization from SPIG is stored to SLMA and it is supported without additional signaling.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.657-659
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• 2004

CAN is a serial communication bus for real-time controls and automations in distributed control systems. In distributed control systems, occasionally a sensor module and a controller are not in the same node and physically separated. In order for the signal from a sensor node to reach the controller node, the signal must travel through network. CAN has a certain capabilities to deal with real-time data. However, when many nodes on the networks try to send data on the same network, the arbitration mechanism to solve the data collision problem is necessary. This situation causes the time delay which has detrimental effects on the performance of the control systems. This paper proposes a method to solve the problem due to the time delay in distributed control system using CAN. Time delay is approximated to an element with a rational transfer function using Pade approximation and Mu~synthesis method is applied. Since time delay in the network is not constant, the time delay element is considered to be an uncertainty block with a bound. The proposed method is applied to the experimental system with CAN and proved to be effective.

• Proceedings of the Korean Geotechical Society Conference
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• 2006.03a
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• pp.1100-1109
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• 2006

In this study, two different technologies which can measure temperature simultaneously at many points are introduced. One is to use a thermal sensor cable that is comprised of addressable thermal sensors connected in parallel within a single cable. The other is to use an optic fiber with Distributed Temperature Sensing (DTS) system. The difference between two technologies can be summarized as follows. A thermal sensor cable has a concept of 'point sensing' that can measure temperature at accurate position of a thermal sensor. So the accuracy and resolution of temperature measurement are up to the ability of the thermal sensor. Whereas optic fiber sensor has a concept of 'distributed sensing' because temperature is measured by ratio of Stokes and anti-Stokes component intensities of Raman backscatter that is generated when laser pulse travels along an optic fiber. It's resolution is determined by measuring distance, measuring time and spatial resolution. The purpose of this study is that application targets of two temperature measurement techniques are checked in technical and economical phases by examining the strength and weakness of them. Considering the functions and characteristics of two techniques, the thermal sensor cable will be suitable to apply to the assessment of groundwater flow, geothermal distribution and grouting efficiency within 300m distance. It is expected that the optic fiber sensor can be widely utilized at various fields (for example: pipe line inspection, tunnel fire detection, power line monitoring etc.) which need an information of temperature distribution over relatively long distance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.9
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• pp.3515-3538
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• 2015

Data accuracy can be increased by detecting and removing the incorrect data generated in wireless sensor networks. By increasing the data accuracy, network lifetime can be increased parallel. Network lifetime or operational time is the time during which WSN is able to fulfill its tasks by using microcontroller with on-chip memory radio transceivers, albeit distributed sensor nodes send summary of their data to their cluster heads, which reduce energy consumption gradually. In this paper a powerful algorithm using proactive fuzzy system is proposed and it is a mixture of fuzzy logic with comparative correlation techniques that ensure high data accuracy by detecting incorrect data in distributed wireless sensor networks. This proposed system is implemented in two phases there, the first phase creates input space partitioning by using robust fuzzy c means clustering and the second phase detects incorrect data and removes it completely. Experimental result makes transparent of combined correlated fuzzy system (CCFS) which detects faulty readings with greater accuracy (99.21%) than the existing one (98.33%) along with low false alarm rate.

• Journal of Platform Technology
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• v.9 no.2
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• pp.3-9
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• 2021

The management of sensor information requires the functions for registering, modifying and deleting rapidly sensor information about various many sensors. In this research, Celery and MongoDB are used for developing a sensory data management system. Celery supplies a queue structure based on asynchronous communication in Python. Celery is a distributed simple job-queue but reliable distributed system suitable for processing large message. MongoDB is a NoSQL database that is capable of managing various informal information. In this experiment, we have checked that variety of sensor information can be processed with this system in a IoT environment. To improve the performance for handling a message with sensory data, this system will be deployed in the edge of a cloud infrastructure.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.944-946
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• 2014

Localization of a sensor network node using machine learning has been recently studied. It is easy for Support vector machines algorithm to implement in high level language enabling parallelism. In this paper, we realized Support vector machine using python language and built a sensor network cluster with 5 Pi's. We also established a Hadoop software framework to employ MapReduce mechanism. We modified the existing Support vector machine algorithm to fit into the distributed hadoop architecture system for localization of a sensor node. In our experiment, we implemented the test sensor network with a variety of parameters and examined based on proficiency, resource evaluation, and processing time.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.9B
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• pp.792-802
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• 2006

In this paper, we propose an energy-efficient proactive data dissemination protocol with relatively low delay to cope well with highly mobile sink environments in sensor networks. In order for a dissemination tree to continuously pursue a dynamic sink, we exploit two novel algorithms: forward sink advertisement and distributed fast recovery. In our protocol, the tree is shared with the other slave sinks so that we call it Dynamic Shared Tree (DST) protocol. DST can conserve considerable amount of energy despite maintaining robust connection from all sources to sinks, since tree maintenance of DST is accomplished by just distributed local exchanges. In addition, since the DST is a kindof sink-oriented tree, each source on the DST disseminates data with lower delay along the tree and it also facilitates in-network processing. Through simulations, it is shown that the presented DST is considerably energy-efficient, robust protocol with low delay compared to Directed Diffusion, TTDD, and SEAD, in highly mobile sink environment.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.5
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• pp.503-510
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• 2016

In this paper I analyzed two decoding algorithms of decentralized LT codes for distributed storage by simulations in small-scale wireless sensor network. From the simulation results we can see that when the decoding ration is above 2.0, the successful decoding probability of OBP decoding is about 99%, while that of BP decoding is below 50% with n=100, and about 70% with n=200. We showed that OBP decoding algorithm is an efficient decoding scheme for decentralized LT codes for distributed storage in small-scale wireless sensor network.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.10
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• pp.1776-1786
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• 2010

Intelligent distribution automation system have total monitoring and control capability. The system covers substation, distribution network, distributed generations and customers at HV system. Various intelligent distribution facilities installed at distribution systems have voltage sensor, current sensor, aging monitoring sensor. Intelligent Feeder Remote Terminal Unit (IFRTU) tied to intelligent distribution facilities process information from facilities and it checks information of fault, power quality and aging of distribution facilities. The information is transmitted to master station through communication line. The master station have remote monitoring system covers substation, distribution network, distributed generations and customers. It also have various application programs that maintain optimal network operation by using information from on-site devices.